A quantitative assessment of the insider/outsider dimension of the cultural theory of risk and place
Bibliographic record
Abstract
This paper examines two hypotheses of risk perception: cultural theory's distinction between insiders and outsiders and the idea that risk perceptions and their determinants differ substantially from one place to the next for the same point‐source hazard. These hypotheses are juxtaposed in cross‐tabulations and logistic regression models with competing explanations of perceived risk in communities living with technological environmental hazards: sound management, benefits, fair facility siting and sociodemographics. The data come from a telephone survey of 455 residents in Swan Hills (n = 173), Fort Assiniboine (n = 171) and Kinuso (n = 111), Alberta, Canada who are all near a large‐scale hazardous waste treatment facility. Considerable support is found for the insider/outsider thesis in terms of the highest ranked information sources and trust to ensure safety. Place differences are clear where, for example, the least facility‐related concern is in Swan Hills (31%) 12 km away, the highest is in Kinuso (81%) 70 km away and moderately high concern is in Fort Assiniboine (62%) which is also 70 km away. This study highlights the importance of fair facility siting, the need to go beyond cultural bias analysis when studying the cultural theory of risk, and suggests further exploration of the notion of tailoring risk communication that is place specific, and emphasizes channels that may be defined as ‘outsider’ and ‘insider’.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".